---
res:
  bibo_abstract:
  - Genomic measurements of DNA methylation, gene expression or protein levels are
    becoming more prevalent and are increasingly used to study health outcomes. However,
    most proposed association testing methods consider only marginal effects of each
    feature on a single outcome variable and are not set up to handle highly correlated,
    continuous data. Here, we introduce MAJA, a method to learn shared and outcome-specific
    effects for multiple traits in multi-omics data. MAJA determines the unique contribution
    of individual loci, genes, or molecular pathways to variation in one or more traits,
    conditional on all other measured “omics” data genome-wide. Simulations show MAJA
    accurately finds shared and distinct associations between omics-data and multiple
    traits and estimates omics-specific (co)variances, allowing for sparsity and correlations
    within the data. Applying MAJA to 12 outcome traits in Generation Scotland methylation
    data (n = 18 264), we find novel shared epigenetic probes among cholesterol metabolism,
    osteoarthritis, blood pressure and asthma. In contrast to marginal testing, we
    find only 10 CpG probes with significant effects above the genome-wide background.
    This highlights the need for joint association testing in highly correlated methylation
    data from whole blood and for studies of increased sample size in order to refine
    epigenomic associations in observational data.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Ilse
      foaf_name: Krätschmer, Ilse
      foaf_surname: Krätschmer
      foaf_workInfoHomepage: http://www.librecat.org/personId=30d4014e-7753-11eb-b44b-db6d61112e73
    orcid: 0000-0002-5636-9259
  - foaf_Person:
      foaf_givenName: Hannah M
      foaf_name: Smith, Hannah M
      foaf_surname: Smith
  - foaf_Person:
      foaf_givenName: Daniel L
      foaf_name: McCartney, Daniel L
      foaf_surname: McCartney
  - foaf_Person:
      foaf_givenName: Elena
      foaf_name: Bernabeu, Elena
      foaf_surname: Bernabeu
  - foaf_Person:
      foaf_givenName: Mahdi
      foaf_name: Mahmoudi, Mahdi
      foaf_surname: Mahmoudi
  - foaf_Person:
      foaf_givenName: Archie
      foaf_name: Campbell, Archie
      foaf_surname: Campbell
  - foaf_Person:
      foaf_givenName: Janie
      foaf_name: Corley, Janie
      foaf_surname: Corley
  - foaf_Person:
      foaf_givenName: Sarah E
      foaf_name: Harris, Sarah E
      foaf_surname: Harris
  - foaf_Person:
      foaf_givenName: Simon R
      foaf_name: Cox, Simon R
      foaf_surname: Cox
  - foaf_Person:
      foaf_givenName: Riccardo E
      foaf_name: Marioni, Riccardo E
      foaf_surname: Marioni
  - foaf_Person:
      foaf_givenName: Matthew Richard
      foaf_name: Robinson, Matthew Richard
      foaf_surname: Robinson
      foaf_workInfoHomepage: http://www.librecat.org/personId=E5D42276-F5DA-11E9-8E24-6303E6697425
    orcid: 0000-0001-8982-8813
  bibo_doi: 10.1093/bioadv/vbag231
  bibo_issue: '1'
  bibo_volume: 6
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2635-0041
  dct_language: eng
  dct_publisher: Oxford University Press@
  dct_title: 'MAJA: Multivariate Bayesian model for discovery of shared epigenetic
    pathways across human phenotypes@'
  fabio_hasPubmedId: '42761413'
...